"... In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry and government, integrated their research in a single robot named GRACE. This paper describes this first year effort by the GRACE team, and describes not only the variou ..."

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry and government, integrated their research in a single robot named GRACE. This paper describes this first year effort by the GRACE team, and describes not only the various techniques each participant brought to GRACE, but also the difficult integration effort itself.

"... Recent research in human-robot interaction has investigated the idea of Sliding, or Adjustable, Autonomy, a mode of operation bridging the gap between complete robot autonomy and full teleoperation. This work, by and large, has been in single-agent domains – involving only one human and one robot – ..."

Recent research in human-robot interaction has investigated the idea of Sliding, or Adjustable, Autonomy, a mode of operation bridging the gap between complete robot autonomy and full teleoperation. This work, by and large, has been in single-agent domains – involving only one human and one robot – and has not examined the issues that arise when moving to multi-agent domains. Here, we discuss the issues involved when adapting Sliding Autonomy concepts to coordinated multi-agent teams. In our system, remote human operators have the ability to join, or leave, the team at will, to assist the autonomous agents with their tasks while not disrupting the team’s coordination. We employ user modeling in order to allow agents to request help when appropriate, regardless of whether human operators are actively monitoring their progress. To validate our approach, we present the results of two experiments. The first evaluates the human–multi-robot team’s performance under four different collaboration strategies including complete teleoperation, pure autonomy, and two distinct versions of Sliding Autonomy. The second experiment compares a variety of user interface configurations, to investigate how quickly a human operator can attain situational awareness when asked to help. The results of these studies support our belief that by incorporating a remote human operator into multi-agent teams, the team as a whole becomes more robust and efficient. I.

"... We are developing a coordinated team of robots to assemble structures. The assembly tasks are sufficiently complex that no single robot, or type of robot, can complete the assembly alone. Even with a group of multiple heterogeneous robots, each adding its unique set of capabilities to the system, th ..."

We are developing a coordinated team of robots to assemble structures. The assembly tasks are sufficiently complex that no single robot, or type of robot, can complete the assembly alone. Even with a group of multiple heterogeneous robots, each adding its unique set of capabilities to the system, the number of contingencies that must be addressed for a completely autonomous system is prohibitively large. Tele-operating a multiple robot system, at the other extreme, is difficult and performance may be highly dependent on the skill of the operator. We propose and evaluate an implementation of a framework that, ideally, provides the operator with a means to interact seamlessly with the autonomous control system. Using an architecture that incorporates sliding autonomy, the operator can augment autonomous control by providing input to help the system recover from unexpected errors and increase system efficiency. Our implementation is motivated by results from an extended series of experiments we are conducting with three robots that work together to dock both ends of a suspended beam.

"... Robotic exploration tasks involve inherent uncertainty. They typically include navigating through unknown terrain, searching for features that may or may not be present, and intelligently reacting to data from noisy sensors (for example, a search and rescue robot, believing it has detected a trapped ..."

Robotic exploration tasks involve inherent uncertainty. They typically include navigating through unknown terrain, searching for features that may or may not be present, and intelligently reacting to data from noisy sensors (for example, a search and rescue robot, believing it has detected a trapped earthquake victim, might stop to check for signs of life). Exploration domains are distinguished both by the prevalence of uncertainty and by the importance of intelligent information gathering. An exploring robot must understand what unknown information is most relevant to its goals, how to gather that information, and how to incorporate the results into its future actions. This thesis has two main components. First, we present planning algorithms that generate robot control policies for partially observable Markov decision process (POMDP) planning problems. POMDP models explicitly represent the uncertain state of the world using a probability distribution over possible states, and they allow the planner to reason about information gathering actions in a way that is decision theoretically optimal. Relative to existing POMDP planning algorithms, our algorithms can more quickly generate approximately optimal policies, taking advantage of innovations in efficient value function representation, heuristic search, and state abstraction. This improved POMDP planning is important both to exploration domains and to a wider class of decision problems. Second, we demonstrate the relevance of onboard science data analysis and POMDP planning to robotic exploration. Our experiments centered around a robot deployed to map the distribution of life in the Atacama Desert of Chile, using operational techniques similar to a Mars mission. We found that science autonomy and POMDP planning techniques significantly improved science yield for exploration tasks conducted both in simulation and onboard the robot. 2

"... We have developed a software architecture for teams of robots and humans to jointly perform tightly coordinated tasks, such as assembly of structures in orbit or on planetary surfaces. While we envision that robots will autonomously perform such work in the future, the state of the art falls short o ..."

We have developed a software architecture for teams of robots and humans to jointly perform tightly coordinated tasks, such as assembly of structures in orbit or on planetary surfaces. While we envision that robots will autonomously perform such work in the future, the state of the art falls short of the capabilities necessary to handle all possible contingencies. Our architecture provides a principled methodology for human involvement to optimize both task efficiency and robustness by combining robot capability with human intuition. We call such mixed control strategies "sliding autonomy". Robots accomplish as many of the tasks as they can autonomously, and human operators take over control to perform those that cannot be easily automated or to provide help when the robots fail. In this paper, we discuss results from recent experiments that quantify the effect of different levels of autonomy on the system's overall performance. By introducing two modes of sliding autonomy, we are able to achieve the high reliability of a teleoperated system combined with the high efficiency of autonomous operation. The incurred mental demand of the operator is directly proportional to the increase in system efficiency.

"... In this manuscript, we discuss new solutions for mechanical design and motion planning for a class of three-dimensional modular self-reconfigurable robotic system, namely I-Cubes. This system is a bipartite collection of active links that provide motions for self-reconfiguration, and cubes acting as ..."

In this manuscript, we discuss new solutions for mechanical design and motion planning for a class of three-dimensional modular self-reconfigurable robotic system, namely I-Cubes. This system is a bipartite collection of active links that provide motions for self-reconfiguration, and cubes acting as connection points. The links are three degree of freedom manipulators that can attach to and detach from the cube faces. The cubes can be positioned and oriented using the links. These capabilities enable the system to change its shape and perform locomotion tasks over difficult terrain. This paper describes the scaled down version of the system previously described in [1] and details the new design and manufacturing approaches. Initially designed algorithms for motion planning of I-Cubes are improved to provide better results. Results of our tests are given and issues related to motion planning are discussed. The user interfaces designed for the control of the system and algorithm evaluation is also described.

"... This paper presents a robot search task (social tag) that uses social interaction, in the form of asking for help, as an integral component of task completion. Socially distributed perception is defined as a robot’s ability to augment its limited sensory capacities through social interaction. We des ..."

This paper presents a robot search task (social tag) that uses social interaction, in the form of asking for help, as an integral component of task completion. Socially distributed perception is defined as a robot’s ability to augment its limited sensory capacities through social interaction. We describe the task of social tag and its implementation on the robot GRACE for the AAAI 2005 Mobile Robot Competition & Exhibition. We then discuss our observations and analyses of GRACE’s performance as a situated interaction with conference participants. Our results suggest we were successful in promoting a form of social interaction that allowed people to help the robot achieve its goal. Furthermore, we found that different social uses of the physical space had an effect on the nature of the interaction. Finally, we discuss the implications of this design approach for effective and compelling human-robot interaction, considering its relationship to concepts such as dependency, mixed initiative, and socially distributed cognition. Keywords Social robotics. Human-robot interaction. Social situatedness. Socially distributed cognition. Mixed initiative. Observational analysis. Multidisciplinary design

"... The complexity of heterogeneous robotic teams and the domains in which they are deployed is fast outstripping the ability of autonomous control software to handle the myriad failure modes inherent in such systems. As a result, remote human operators are being brought into the teams as equal members ..."

The complexity of heterogeneous robotic teams and the domains in which they are deployed is fast outstripping the ability of autonomous control software to handle the myriad failure modes inherent in such systems. As a result, remote human operators are being brought into the teams as equal members via sliding autonomy to increase the robustness and effectiveness of such teams. A principled approach to deciding when to request help from the human will benefit such systems by allowing them to efficiently make use of the human partner. We have developed a cost-benefit analysis framework and models of both autonomous system and user in order to enable such principled decisions. In addition, we have conducted user experiments to determine the proper form for the learning curve component of the human’s model. The resulting automated analysis is able to predict the performance of both the autonomous system and the human in order to assign responsibility for tasks to one or the other.

"... Abstract — Autonomous systems are efficient but often unreliable. In domains where reliability is paramount, efficiency is sacrificed by putting an operator in control via teleoperation. We are investigating a mode of shared control called “Sliding Autonomy ” that combines the efficiency of autonomy ..."

Abstract — Autonomous systems are efficient but often unreliable. In domains where reliability is paramount, efficiency is sacrificed by putting an operator in control via teleoperation. We are investigating a mode of shared control called “Sliding Autonomy ” that combines the efficiency of autonomy and the reliability of human control in the performance of complex tasks, such as the assembly of large structures by a team of robots. Here we introduce an approach based on Markov models that captures interdependencies between the team members and predicts system performance. We report results from a study in which three robots work cooperatively with an operator to assemble a structure. The scenario requires high precision and has a large number of failure modes. Our results support both our expectations and modeling and show that our combined robot-human team is able to perform the assembly at a level of efficiency approaching that of fully autonomous operation while increasing overall reliability to near-teleoperation levels. This increase in performance is achieved while simultaneously reducing mental operator workload. I.

"... This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.1 lb network. Our approach uses data labeled by ground truth position to learn a probabilistic mapping from locations to wireless signals, represented by piecewise linear Gaussian ..."

This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.1 lb network. Our approach uses data labeled by ground truth position to learn a probabilistic mapping from locations to wireless signals, represented by piecewise linear Gaussians. It then uses sequences of wireless signal data (without position labels) to acquire motion models of individual people, which further improves the localization accuracy. The approach has been implemented and evaluated in an office environment. 1